Overview

Dataset statistics

Number of variables31
Number of observations602987
Missing cells3033695
Missing cells (%)16.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory142.6 MiB
Average record size in memory248.0 B

Variable types

Numeric20
Categorical5
Text6

Alerts

CANCELLED is highly imbalanced (88.6%)Imbalance
DIVERTED is highly imbalanced (97.2%)Imbalance
DEP_TIME has 8883 (1.5%) missing valuesMissing
DEP_DELAY has 8886 (1.5%) missing valuesMissing
TAXI_OUT has 9137 (1.5%) missing valuesMissing
TAXI_IN has 9272 (1.5%) missing valuesMissing
ARR_TIME has 9272 (1.5%) missing valuesMissing
ARR_DELAY has 10845 (1.8%) missing valuesMissing
CANCELLATION_CODE has 593815 (98.5%) missing valuesMissing
AIR_TIME has 10845 (1.8%) missing valuesMissing
CARRIER_DELAY has 474548 (78.7%) missing valuesMissing
WEATHER_DELAY has 474548 (78.7%) missing valuesMissing
NAS_DELAY has 474548 (78.7%) missing valuesMissing
SECURITY_DELAY has 474548 (78.7%) missing valuesMissing
LATE_AIRCRAFT_DELAY has 474548 (78.7%) missing valuesMissing
SECURITY_DELAY is highly skewed (γ1 = 101.1322251)Skewed
DEP_DELAY has 26621 (4.4%) zerosZeros
ARR_DELAY has 10607 (1.8%) zerosZeros
CARRIER_DELAY has 55325 (9.2%) zerosZeros
WEATHER_DELAY has 120952 (20.1%) zerosZeros
NAS_DELAY has 72188 (12.0%) zerosZeros
SECURITY_DELAY has 127603 (21.2%) zerosZeros
LATE_AIRCRAFT_DELAY has 59380 (9.8%) zerosZeros

Reproduction

Analysis started2024-03-30 05:41:44.711952
Analysis finished2024-03-30 05:45:12.112895
Duration3 minutes and 27.4 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

DAY_OF_WEEK
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8849843
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T02:45:12.337655image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9382999
Coefficient of variation (CV)0.49892091
Kurtosis-1.146095
Mean3.8849843
Median Absolute Deviation (MAD)2
Skewness0.12021948
Sum2342595
Variance3.7570065
MonotonicityIncreasing
2024-03-30T02:45:12.629717image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 100666
16.7%
3 96397
16.0%
2 95349
15.8%
5 80493
13.3%
1 80312
13.3%
7 78645
13.0%
6 71125
11.8%
ValueCountFrequency (%)
1 80312
13.3%
2 95349
15.8%
3 96397
16.0%
4 100666
16.7%
5 80493
13.3%
6 71125
11.8%
7 78645
13.0%
ValueCountFrequency (%)
7 78645
13.0%
6 71125
11.8%
5 80493
13.3%
4 100666
16.7%
3 96397
16.0%
2 95349
15.8%
1 80312
13.3%

FL_DATE
Categorical

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
8/3/2023 12:00:00 AM
 
20435
8/10/2023 12:00:00 AM
 
20394
8/4/2023 12:00:00 AM
 
20378
8/11/2023 12:00:00 AM
 
20356
8/7/2023 12:00:00 AM
 
20318
Other values (26)
501106 

Length

Max length21
Median length21
Mean length20.707466
Min length20

Characters and Unicode

Total characters12486333
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8/7/2023 12:00:00 AM
2nd row8/7/2023 12:00:00 AM
3rd row8/7/2023 12:00:00 AM
4th row8/7/2023 12:00:00 AM
5th row8/7/2023 12:00:00 AM

Common Values

ValueCountFrequency (%)
8/3/2023 12:00:00 AM 20435
 
3.4%
8/10/2023 12:00:00 AM 20394
 
3.4%
8/4/2023 12:00:00 AM 20378
 
3.4%
8/11/2023 12:00:00 AM 20356
 
3.4%
8/7/2023 12:00:00 AM 20318
 
3.4%
8/14/2023 12:00:00 AM 20280
 
3.4%
8/17/2023 12:00:00 AM 19989
 
3.3%
8/31/2023 12:00:00 AM 19927
 
3.3%
8/18/2023 12:00:00 AM 19923
 
3.3%
8/24/2023 12:00:00 AM 19921
 
3.3%
Other values (21) 401066
66.5%

Length

2024-03-30T02:45:13.020904image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12:00:00 602987
33.3%
am 602987
33.3%
8/3/2023 20435
 
1.1%
8/10/2023 20394
 
1.1%
8/4/2023 20378
 
1.1%
8/11/2023 20356
 
1.1%
8/7/2023 20318
 
1.1%
8/14/2023 20280
 
1.1%
8/17/2023 19989
 
1.1%
8/31/2023 19927
 
1.1%
Other values (23) 440910
24.4%

Most occurring characters

ValueCountFrequency (%)
0 3074006
24.6%
2 2058591
16.5%
/ 1205974
 
9.7%
1205974
 
9.7%
: 1205974
 
9.7%
1 876756
 
7.0%
3 701291
 
5.6%
8 662030
 
5.3%
A 602987
 
4.8%
M 602987
 
4.8%
Other values (5) 289763
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12486333
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3074006
24.6%
2 2058591
16.5%
/ 1205974
 
9.7%
1205974
 
9.7%
: 1205974
 
9.7%
1 876756
 
7.0%
3 701291
 
5.6%
8 662030
 
5.3%
A 602987
 
4.8%
M 602987
 
4.8%
Other values (5) 289763
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12486333
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3074006
24.6%
2 2058591
16.5%
/ 1205974
 
9.7%
1205974
 
9.7%
: 1205974
 
9.7%
1 876756
 
7.0%
3 701291
 
5.6%
8 662030
 
5.3%
A 602987
 
4.8%
M 602987
 
4.8%
Other values (5) 289763
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12486333
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3074006
24.6%
2 2058591
16.5%
/ 1205974
 
9.7%
1205974
 
9.7%
: 1205974
 
9.7%
1 876756
 
7.0%
3 701291
 
5.6%
8 662030
 
5.3%
A 602987
 
4.8%
M 602987
 
4.8%
Other values (5) 289763
 
2.3%
Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
WN
125664 
DL
89057 
AA
85157 
UA
66801 
OO
58093 
Other values (10)
178215 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1205974
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9E
2nd row9E
3rd row9E
4th row9E
5th row9E

Common Values

ValueCountFrequency (%)
WN 125664
20.8%
DL 89057
14.8%
AA 85157
14.1%
UA 66801
11.1%
OO 58093
9.6%
YX 24333
 
4.0%
AS 22974
 
3.8%
B6 22895
 
3.8%
NK 21514
 
3.6%
MQ 20359
 
3.4%
Other values (5) 66140
11.0%

Length

2024-03-30T02:45:13.368688image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wn 125664
20.8%
dl 89057
14.8%
aa 85157
14.1%
ua 66801
11.1%
oo 58093
9.6%
yx 24333
 
4.0%
as 22974
 
3.8%
b6 22895
 
3.8%
nk 21514
 
3.6%
mq 20359
 
3.4%
Other values (5) 66140
11.0%

Most occurring characters

ValueCountFrequency (%)
A 267113
22.1%
N 147178
12.2%
O 132963
11.0%
W 125664
10.4%
D 89057
 
7.4%
L 89057
 
7.4%
U 66801
 
5.5%
9 33526
 
2.8%
Y 24333
 
2.0%
X 24333
 
2.0%
Other values (11) 205949
17.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1205974
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 267113
22.1%
N 147178
12.2%
O 132963
11.0%
W 125664
10.4%
D 89057
 
7.4%
L 89057
 
7.4%
U 66801
 
5.5%
9 33526
 
2.8%
Y 24333
 
2.0%
X 24333
 
2.0%
Other values (11) 205949
17.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1205974
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 267113
22.1%
N 147178
12.2%
O 132963
11.0%
W 125664
10.4%
D 89057
 
7.4%
L 89057
 
7.4%
U 66801
 
5.5%
9 33526
 
2.8%
Y 24333
 
2.0%
X 24333
 
2.0%
Other values (11) 205949
17.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1205974
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 267113
22.1%
N 147178
12.2%
O 132963
11.0%
W 125664
10.4%
D 89057
 
7.4%
L 89057
 
7.4%
U 66801
 
5.5%
9 33526
 
2.8%
Y 24333
 
2.0%
X 24333
 
2.0%
Other values (11) 205949
17.1%

OP_CARRIER_FL_NUM
Real number (ℝ)

Distinct5919
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2338.6403
Minimum1
Maximum8810
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T02:45:13.796598image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile286
Q11048
median2094
Q33410
95-th percentile5388
Maximum8810
Range8809
Interquartile range (IQR)2362

Descriptive statistics

Standard deviation1580.5123
Coefficient of variation (CV)0.67582531
Kurtosis-0.66989294
Mean2338.6403
Median Absolute Deviation (MAD)1146
Skewness0.564843
Sum1.4101697 × 109
Variance2498019.1
MonotonicityNot monotonic
2024-03-30T02:45:14.279819image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
648 351
 
0.1%
323 339
 
0.1%
358 316
 
0.1%
1150 310
 
0.1%
555 302
 
0.1%
677 301
 
< 0.1%
591 298
 
< 0.1%
777 297
 
< 0.1%
312 297
 
< 0.1%
354 293
 
< 0.1%
Other values (5909) 599883
99.5%
ValueCountFrequency (%)
1 193
< 0.1%
2 172
< 0.1%
3 97
< 0.1%
4 145
< 0.1%
5 99
< 0.1%
6 77
 
< 0.1%
7 191
< 0.1%
8 104
< 0.1%
9 164
< 0.1%
10 198
< 0.1%
ValueCountFrequency (%)
8810 1
 
< 0.1%
8789 1
 
< 0.1%
8788 1
 
< 0.1%
8785 2
 
< 0.1%
8784 4
< 0.1%
8783 4
< 0.1%
8771 5
< 0.1%
8770 8
< 0.1%
6856 2
 
< 0.1%
6688 2
 
< 0.1%

ORIGIN_AIRPORT_ID
Real number (ℝ)

Distinct337
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12640.889
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T02:45:14.686692image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q313964
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2672

Descriptive statistics

Standard deviation1532.9791
Coefficient of variation (CV)0.12127146
Kurtosis-1.3029086
Mean12640.889
Median Absolute Deviation (MAD)1591
Skewness0.11319848
Sum7.6222915 × 109
Variance2350024.9
MonotonicityNot monotonic
2024-03-30T02:45:15.159775image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 29431
 
4.9%
11298 26370
 
4.4%
11292 26308
 
4.4%
13930 23347
 
3.9%
12892 17683
 
2.9%
11057 17319
 
2.9%
14747 15974
 
2.6%
12889 15939
 
2.6%
14107 14304
 
2.4%
12953 14074
 
2.3%
Other values (327) 402238
66.7%
ValueCountFrequency (%)
10135 398
 
0.1%
10136 122
 
< 0.1%
10140 2172
0.4%
10141 62
 
< 0.1%
10146 62
 
< 0.1%
10154 406
 
0.1%
10155 93
 
< 0.1%
10157 88
 
< 0.1%
10158 248
 
< 0.1%
10165 9
 
< 0.1%
ValueCountFrequency (%)
16869 155
 
< 0.1%
16218 171
 
< 0.1%
15991 62
 
< 0.1%
15919 1059
0.2%
15897 60
 
< 0.1%
15841 62
 
< 0.1%
15624 855
0.1%
15607 62
 
< 0.1%
15582 53
 
< 0.1%
15569 53
 
< 0.1%

ORIGIN
Text

Distinct337
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2024-03-30T02:45:17.057274image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1808961
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRIC
2nd rowCLT
3rd rowJFK
4th rowSYR
5th rowBHM
ValueCountFrequency (%)
atl 29431
 
4.9%
dfw 26370
 
4.4%
den 26308
 
4.4%
ord 23347
 
3.9%
lax 17683
 
2.9%
clt 17319
 
2.9%
sea 15974
 
2.6%
las 15939
 
2.6%
phx 14304
 
2.4%
lga 14074
 
2.3%
Other values (327) 402238
66.7%
2024-03-30T02:45:18.079072image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 206729
 
11.4%
L 165469
 
9.1%
S 154148
 
8.5%
D 145224
 
8.0%
T 95173
 
5.3%
O 92352
 
5.1%
C 91863
 
5.1%
M 81220
 
4.5%
F 75118
 
4.2%
W 71351
 
3.9%
Other values (16) 630314
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1808961
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 206729
 
11.4%
L 165469
 
9.1%
S 154148
 
8.5%
D 145224
 
8.0%
T 95173
 
5.3%
O 92352
 
5.1%
C 91863
 
5.1%
M 81220
 
4.5%
F 75118
 
4.2%
W 71351
 
3.9%
Other values (16) 630314
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1808961
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 206729
 
11.4%
L 165469
 
9.1%
S 154148
 
8.5%
D 145224
 
8.0%
T 95173
 
5.3%
O 92352
 
5.1%
C 91863
 
5.1%
M 81220
 
4.5%
F 75118
 
4.2%
W 71351
 
3.9%
Other values (16) 630314
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1808961
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 206729
 
11.4%
L 165469
 
9.1%
S 154148
 
8.5%
D 145224
 
8.0%
T 95173
 
5.3%
O 92352
 
5.1%
C 91863
 
5.1%
M 81220
 
4.5%
F 75118
 
4.2%
W 71351
 
3.9%
Other values (16) 630314
34.8%
Distinct331
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2024-03-30T02:45:18.591748image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.045402
Min length8

Characters and Unicode

Total characters7866208
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRichmond, VA
2nd rowCharlotte, NC
3rd rowNew York, NY
4th rowSyracuse, NY
5th rowBirmingham, AL
ValueCountFrequency (%)
ca 65593
 
4.7%
tx 64539
 
4.6%
fl 48079
 
3.4%
il 32293
 
2.3%
ny 32086
 
2.3%
san 32015
 
2.3%
ga 31445
 
2.2%
chicago 31237
 
2.2%
atlanta 29431
 
2.1%
co 29204
 
2.1%
Other values (402) 1006171
71.8%
2024-03-30T02:45:19.363868image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
799106
 
10.2%
, 602987
 
7.7%
a 600313
 
7.6%
o 434154
 
5.5%
e 415330
 
5.3%
n 387029
 
4.9%
t 379152
 
4.8%
l 350472
 
4.5%
i 300076
 
3.8%
r 283229
 
3.6%
Other values (47) 3314360
42.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7866208
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
799106
 
10.2%
, 602987
 
7.7%
a 600313
 
7.6%
o 434154
 
5.5%
e 415330
 
5.3%
n 387029
 
4.9%
t 379152
 
4.8%
l 350472
 
4.5%
i 300076
 
3.8%
r 283229
 
3.6%
Other values (47) 3314360
42.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7866208
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
799106
 
10.2%
, 602987
 
7.7%
a 600313
 
7.6%
o 434154
 
5.5%
e 415330
 
5.3%
n 387029
 
4.9%
t 379152
 
4.8%
l 350472
 
4.5%
i 300076
 
3.8%
r 283229
 
3.6%
Other values (47) 3314360
42.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7866208
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
799106
 
10.2%
, 602987
 
7.7%
a 600313
 
7.6%
o 434154
 
5.5%
e 415330
 
5.3%
n 387029
 
4.9%
t 379152
 
4.8%
l 350472
 
4.5%
i 300076
 
3.8%
r 283229
 
3.6%
Other values (47) 3314360
42.1%
Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2024-03-30T02:45:20.122490image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.178576
Min length4

Characters and Unicode

Total characters4931575
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVirginia
2nd rowNorth Carolina
3rd rowNew York
4th rowNew York
5th rowAlabama
ValueCountFrequency (%)
california 65593
 
9.5%
texas 64539
 
9.3%
florida 48079
 
7.0%
new 47086
 
6.8%
illinois 32293
 
4.7%
york 32086
 
4.6%
carolina 31663
 
4.6%
georgia 31445
 
4.6%
colorado 29204
 
4.2%
north 27359
 
4.0%
Other values (51) 281322
40.7%
2024-03-30T02:45:20.751831image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 662896
13.4%
i 553735
 
11.2%
o 472650
 
9.6%
n 369976
 
7.5%
r 351577
 
7.1%
e 300814
 
6.1%
s 286836
 
5.8%
l 272845
 
5.5%
C 128292
 
2.6%
t 120586
 
2.4%
Other values (37) 1411368
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4931575
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 662896
13.4%
i 553735
 
11.2%
o 472650
 
9.6%
n 369976
 
7.5%
r 351577
 
7.1%
e 300814
 
6.1%
s 286836
 
5.8%
l 272845
 
5.5%
C 128292
 
2.6%
t 120586
 
2.4%
Other values (37) 1411368
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4931575
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 662896
13.4%
i 553735
 
11.2%
o 472650
 
9.6%
n 369976
 
7.5%
r 351577
 
7.1%
e 300814
 
6.1%
s 286836
 
5.8%
l 272845
 
5.5%
C 128292
 
2.6%
t 120586
 
2.4%
Other values (37) 1411368
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4931575
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 662896
13.4%
i 553735
 
11.2%
o 472650
 
9.6%
n 369976
 
7.5%
r 351577
 
7.1%
e 300814
 
6.1%
s 286836
 
5.8%
l 272845
 
5.5%
C 128292
 
2.6%
t 120586
 
2.4%
Other values (37) 1411368
28.6%

ORIGIN_WAC
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.728618
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T02:45:21.070525image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q134
median45
Q382
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)48

Descriptive statistics

Standard deviation26.917018
Coefficient of variation (CV)0.49182712
Kurtosis-1.308255
Mean54.728618
Median Absolute Deviation (MAD)23
Skewness-0.04997092
Sum33000645
Variance724.52587
MonotonicityNot monotonic
2024-03-30T02:45:21.372071image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 65593
 
10.9%
74 64539
 
10.7%
33 48079
 
8.0%
41 32293
 
5.4%
22 32086
 
5.3%
34 31445
 
5.2%
82 29204
 
4.8%
36 25938
 
4.3%
38 20974
 
3.5%
93 18186
 
3.0%
Other values (42) 234650
38.9%
ValueCountFrequency (%)
1 4060
 
0.7%
2 11661
1.9%
3 3475
 
0.6%
4 403
 
0.1%
5 125
 
< 0.1%
11 1832
 
0.3%
12 1792
 
0.3%
13 13298
2.2%
14 601
 
0.1%
15 1237
 
0.2%
ValueCountFrequency (%)
93 18186
 
3.0%
92 6912
 
1.1%
91 65593
10.9%
88 990
 
0.2%
87 10134
 
1.7%
86 2389
 
0.4%
85 17861
 
3.0%
84 2756
 
0.5%
83 2357
 
0.4%
82 29204
4.8%

DEST_AIRPORT_ID
Real number (ℝ)

Distinct337
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12640.892
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T02:45:21.683275image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q313964
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2672

Descriptive statistics

Standard deviation1532.948
Coefficient of variation (CV)0.12126897
Kurtosis-1.3028379
Mean12640.892
Median Absolute Deviation (MAD)1591
Skewness0.11324882
Sum7.6222937 × 109
Variance2349929.7
MonotonicityNot monotonic
2024-03-30T02:45:22.036010image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 29428
 
4.9%
11298 26371
 
4.4%
11292 26300
 
4.4%
13930 23346
 
3.9%
12892 17685
 
2.9%
11057 17323
 
2.9%
14747 15968
 
2.6%
12889 15937
 
2.6%
14107 14304
 
2.4%
12953 14074
 
2.3%
Other values (327) 402251
66.7%
ValueCountFrequency (%)
10135 398
 
0.1%
10136 122
 
< 0.1%
10140 2171
0.4%
10141 62
 
< 0.1%
10146 62
 
< 0.1%
10154 406
 
0.1%
10155 93
 
< 0.1%
10157 88
 
< 0.1%
10158 248
 
< 0.1%
10165 9
 
< 0.1%
ValueCountFrequency (%)
16869 155
 
< 0.1%
16218 171
 
< 0.1%
15991 62
 
< 0.1%
15919 1059
0.2%
15897 60
 
< 0.1%
15841 62
 
< 0.1%
15624 855
0.1%
15607 62
 
< 0.1%
15582 53
 
< 0.1%
15569 53
 
< 0.1%

DEST
Text

Distinct337
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2024-03-30T02:45:22.674879image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1808961
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMSP
2nd rowJFK
3rd rowCLT
4th rowDTW
5th rowLGA
ValueCountFrequency (%)
atl 29428
 
4.9%
dfw 26371
 
4.4%
den 26300
 
4.4%
ord 23346
 
3.9%
lax 17685
 
2.9%
clt 17323
 
2.9%
sea 15968
 
2.6%
las 15937
 
2.6%
phx 14304
 
2.4%
lga 14074
 
2.3%
Other values (327) 402251
66.7%
2024-03-30T02:45:23.567890image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 206719
 
11.4%
L 165496
 
9.1%
S 154137
 
8.5%
D 145228
 
8.0%
T 95175
 
5.3%
O 92338
 
5.1%
C 91863
 
5.1%
M 81221
 
4.5%
F 75128
 
4.2%
W 71354
 
3.9%
Other values (16) 630302
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1808961
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 206719
 
11.4%
L 165496
 
9.1%
S 154137
 
8.5%
D 145228
 
8.0%
T 95175
 
5.3%
O 92338
 
5.1%
C 91863
 
5.1%
M 81221
 
4.5%
F 75128
 
4.2%
W 71354
 
3.9%
Other values (16) 630302
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1808961
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 206719
 
11.4%
L 165496
 
9.1%
S 154137
 
8.5%
D 145228
 
8.0%
T 95175
 
5.3%
O 92338
 
5.1%
C 91863
 
5.1%
M 81221
 
4.5%
F 75128
 
4.2%
W 71354
 
3.9%
Other values (16) 630302
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1808961
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 206719
 
11.4%
L 165496
 
9.1%
S 154137
 
8.5%
D 145228
 
8.0%
T 95175
 
5.3%
O 92338
 
5.1%
C 91863
 
5.1%
M 81221
 
4.5%
F 75128
 
4.2%
W 71354
 
3.9%
Other values (16) 630302
34.8%
Distinct331
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2024-03-30T02:45:24.165084image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.045575
Min length8

Characters and Unicode

Total characters7866312
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMinneapolis, MN
2nd rowNew York, NY
3rd rowCharlotte, NC
4th rowDetroit, MI
5th rowNew York, NY
ValueCountFrequency (%)
ca 65602
 
4.7%
tx 64535
 
4.6%
fl 48080
 
3.4%
il 32295
 
2.3%
ny 32080
 
2.3%
san 32018
 
2.3%
ga 31442
 
2.2%
chicago 31239
 
2.2%
atlanta 29428
 
2.1%
co 29196
 
2.1%
Other values (402) 1006177
71.8%
2024-03-30T02:45:25.390657image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
799105
 
10.2%
, 602987
 
7.7%
a 600315
 
7.6%
o 434207
 
5.5%
e 415301
 
5.3%
n 387055
 
4.9%
t 379131
 
4.8%
l 350493
 
4.5%
i 300099
 
3.8%
r 283234
 
3.6%
Other values (47) 3314385
42.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7866312
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
799105
 
10.2%
, 602987
 
7.7%
a 600315
 
7.6%
o 434207
 
5.5%
e 415301
 
5.3%
n 387055
 
4.9%
t 379131
 
4.8%
l 350493
 
4.5%
i 300099
 
3.8%
r 283234
 
3.6%
Other values (47) 3314385
42.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7866312
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
799105
 
10.2%
, 602987
 
7.7%
a 600315
 
7.6%
o 434207
 
5.5%
e 415301
 
5.3%
n 387055
 
4.9%
t 379131
 
4.8%
l 350493
 
4.5%
i 300099
 
3.8%
r 283234
 
3.6%
Other values (47) 3314385
42.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7866312
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
799105
 
10.2%
, 602987
 
7.7%
a 600315
 
7.6%
o 434207
 
5.5%
e 415301
 
5.3%
n 387055
 
4.9%
t 379131
 
4.8%
l 350493
 
4.5%
i 300099
 
3.8%
r 283234
 
3.6%
Other values (47) 3314385
42.1%
Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2024-03-30T02:45:25.919326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.1786075
Min length4

Characters and Unicode

Total characters4931594
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMinnesota
2nd rowNew York
3rd rowNorth Carolina
4th rowMichigan
5th rowNew York
ValueCountFrequency (%)
california 65602
 
9.5%
texas 64535
 
9.3%
florida 48080
 
7.0%
new 47078
 
6.8%
illinois 32295
 
4.7%
york 32080
 
4.6%
carolina 31663
 
4.6%
georgia 31442
 
4.6%
colorado 29196
 
4.2%
north 27364
 
4.0%
Other values (51) 281321
40.7%
2024-03-30T02:45:27.027972image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 662920
13.4%
i 553778
 
11.2%
o 472617
 
9.6%
n 370014
 
7.5%
r 351574
 
7.1%
e 300799
 
6.1%
s 286836
 
5.8%
l 272858
 
5.5%
C 128293
 
2.6%
t 120580
 
2.4%
Other values (37) 1411325
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4931594
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 662920
13.4%
i 553778
 
11.2%
o 472617
 
9.6%
n 370014
 
7.5%
r 351574
 
7.1%
e 300799
 
6.1%
s 286836
 
5.8%
l 272858
 
5.5%
C 128293
 
2.6%
t 120580
 
2.4%
Other values (37) 1411325
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4931594
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 662920
13.4%
i 553778
 
11.2%
o 472617
 
9.6%
n 370014
 
7.5%
r 351574
 
7.1%
e 300799
 
6.1%
s 286836
 
5.8%
l 272858
 
5.5%
C 128293
 
2.6%
t 120580
 
2.4%
Other values (37) 1411325
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4931594
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 662920
13.4%
i 553778
 
11.2%
o 472617
 
9.6%
n 370014
 
7.5%
r 351574
 
7.1%
e 300799
 
6.1%
s 286836
 
5.8%
l 272858
 
5.5%
C 128293
 
2.6%
t 120580
 
2.4%
Other values (37) 1411325
28.6%

DEST_WAC
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.727848
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T02:45:27.502647image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q134
median45
Q382
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)48

Descriptive statistics

Standard deviation26.916702
Coefficient of variation (CV)0.49182825
Kurtosis-1.3082437
Mean54.727848
Median Absolute Deviation (MAD)23
Skewness-0.049882221
Sum33000181
Variance724.50885
MonotonicityNot monotonic
2024-03-30T02:45:27.837089image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 65602
 
10.9%
74 64535
 
10.7%
33 48080
 
8.0%
41 32295
 
5.4%
22 32080
 
5.3%
34 31442
 
5.2%
82 29196
 
4.8%
36 25943
 
4.3%
38 20980
 
3.5%
93 18181
 
3.0%
Other values (42) 234653
38.9%
ValueCountFrequency (%)
1 4060
 
0.7%
2 11662
1.9%
3 3472
 
0.6%
4 402
 
0.1%
5 125
 
< 0.1%
11 1832
 
0.3%
12 1793
 
0.3%
13 13299
2.2%
14 600
 
0.1%
15 1237
 
0.2%
ValueCountFrequency (%)
93 18181
 
3.0%
92 6911
 
1.1%
91 65602
10.9%
88 992
 
0.2%
87 10133
 
1.7%
86 2388
 
0.4%
85 17859
 
3.0%
84 2755
 
0.5%
83 2356
 
0.4%
82 29196
4.8%

CRS_DEP_TIME
Real number (ℝ)

Distinct1231
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1337.3947
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T02:45:28.199316image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile600
Q1910
median1326
Q31749
95-th percentile2140
Maximum2359
Range2358
Interquartile range (IQR)839

Descriptive statistics

Standard deviation501.90242
Coefficient of variation (CV)0.37528369
Kurtosis-1.0842398
Mean1337.3947
Median Absolute Deviation (MAD)420
Skewness0.078570331
Sum8.0643163 × 108
Variance251906.04
MonotonicityNot monotonic
2024-03-30T02:45:28.561565image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 11869
 
2.0%
700 8686
 
1.4%
800 4907
 
0.8%
630 3745
 
0.6%
1000 3610
 
0.6%
615 3454
 
0.6%
730 3379
 
0.6%
830 3357
 
0.6%
900 3278
 
0.5%
1100 2931
 
0.5%
Other values (1221) 553771
91.8%
ValueCountFrequency (%)
1 7
 
< 0.1%
4 1
 
< 0.1%
5 4
 
< 0.1%
6 2
 
< 0.1%
8 4
 
< 0.1%
10 63
< 0.1%
12 4
 
< 0.1%
14 4
 
< 0.1%
15 97
< 0.1%
18 3
 
< 0.1%
ValueCountFrequency (%)
2359 1054
0.2%
2358 88
 
< 0.1%
2357 66
 
< 0.1%
2356 79
 
< 0.1%
2355 339
 
0.1%
2354 38
 
< 0.1%
2353 49
 
< 0.1%
2352 43
 
< 0.1%
2351 26
 
< 0.1%
2350 140
 
< 0.1%

DEP_TIME
Real number (ℝ)

MISSING 

Distinct1419
Distinct (%)0.2%
Missing8883
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean1337.9642
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T02:45:28.929488image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile557
Q1908
median1328
Q31756
95-th percentile2154
Maximum2400
Range2399
Interquartile range (IQR)848

Descriptive statistics

Standard deviation519.77457
Coefficient of variation (CV)0.38848166
Kurtosis-0.9853093
Mean1337.9642
Median Absolute Deviation (MAD)424
Skewness0.020838784
Sum7.9488991 × 108
Variance270165.6
MonotonicityNot monotonic
2024-03-30T02:45:29.382157image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 1507
 
0.2%
556 1463
 
0.2%
557 1381
 
0.2%
554 1265
 
0.2%
558 1231
 
0.2%
655 1177
 
0.2%
600 1159
 
0.2%
656 1157
 
0.2%
559 1141
 
0.2%
553 1123
 
0.2%
Other values (1409) 581500
96.4%
(Missing) 8883
 
1.5%
ValueCountFrequency (%)
1 127
< 0.1%
2 102
< 0.1%
3 88
< 0.1%
4 87
< 0.1%
5 99
< 0.1%
6 66
< 0.1%
7 64
< 0.1%
8 71
< 0.1%
9 81
< 0.1%
10 82
< 0.1%
ValueCountFrequency (%)
2400 84
< 0.1%
2359 108
< 0.1%
2358 122
< 0.1%
2357 131
< 0.1%
2356 135
< 0.1%
2355 155
< 0.1%
2354 130
< 0.1%
2353 165
< 0.1%
2352 162
< 0.1%
2351 143
< 0.1%

DEP_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct1230
Distinct (%)0.2%
Missing8886
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean14.245425
Minimum-59
Maximum3445
Zeros26621
Zeros (%)4.4%
Negative333332
Negative (%)55.3%
Memory size4.6 MiB
2024-03-30T02:45:29.703254image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-59
5-th percentile-10
Q1-5
median-2
Q311
95-th percentile87
Maximum3445
Range3504
Interquartile range (IQR)16

Descriptive statistics

Standard deviation60.608499
Coefficient of variation (CV)4.2545941
Kurtosis255.5977
Mean14.245425
Median Absolute Deviation (MAD)5
Skewness11.630124
Sum8463221
Variance3673.3902
MonotonicityNot monotonic
2024-03-30T02:45:30.052305image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 43528
 
7.2%
-4 40213
 
6.7%
-3 38441
 
6.4%
-2 35020
 
5.8%
-6 34433
 
5.7%
-1 31248
 
5.2%
-7 29859
 
5.0%
0 26621
 
4.4%
-8 23776
 
3.9%
-9 17994
 
3.0%
Other values (1220) 272968
45.3%
ValueCountFrequency (%)
-59 1
 
< 0.1%
-53 1
 
< 0.1%
-50 1
 
< 0.1%
-47 1
 
< 0.1%
-46 1
 
< 0.1%
-43 1
 
< 0.1%
-39 3
< 0.1%
-37 1
 
< 0.1%
-36 1
 
< 0.1%
-35 2
< 0.1%
ValueCountFrequency (%)
3445 1
< 0.1%
3013 1
< 0.1%
2969 1
< 0.1%
2938 1
< 0.1%
2928 1
< 0.1%
2894 1
< 0.1%
2826 1
< 0.1%
2728 1
< 0.1%
2690 1
< 0.1%
2663 1
< 0.1%

TAXI_OUT
Real number (ℝ)

MISSING 

Distinct168
Distinct (%)< 0.1%
Missing9137
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean17.263112
Minimum1
Maximum201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T02:45:30.398196image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q112
median15
Q320
95-th percentile33
Maximum201
Range200
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.0961373
Coefficient of variation (CV)0.52691179
Kurtosis25.717888
Mean17.263112
Median Absolute Deviation (MAD)4
Skewness3.5903024
Sum10251699
Variance82.739713
MonotonicityNot monotonic
2024-03-30T02:45:30.746776image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 49112
 
8.1%
13 48646
 
8.1%
14 45738
 
7.6%
11 44963
 
7.5%
15 41771
 
6.9%
16 36566
 
6.1%
10 36516
 
6.1%
17 32187
 
5.3%
18 27644
 
4.6%
9 24929
 
4.1%
Other values (158) 205778
34.1%
ValueCountFrequency (%)
1 6
 
< 0.1%
2 19
 
< 0.1%
3 60
 
< 0.1%
4 190
 
< 0.1%
5 564
 
0.1%
6 2603
 
0.4%
7 7097
 
1.2%
8 14336
 
2.4%
9 24929
4.1%
10 36516
6.1%
ValueCountFrequency (%)
201 1
< 0.1%
199 1
< 0.1%
176 1
< 0.1%
171 1
< 0.1%
168 1
< 0.1%
167 1
< 0.1%
166 1
< 0.1%
165 2
< 0.1%
162 2
< 0.1%
161 2
< 0.1%

TAXI_IN
Real number (ℝ)

MISSING 

Distinct167
Distinct (%)< 0.1%
Missing9272
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean8.2480146
Minimum1
Maximum223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T02:45:31.080302image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q310
95-th percentile19
Maximum223
Range222
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.7648931
Coefficient of variation (CV)0.82018442
Kurtosis56.427251
Mean8.2480146
Median Absolute Deviation (MAD)2
Skewness5.0075367
Sum4896970
Variance45.763778
MonotonicityNot monotonic
2024-03-30T02:45:31.383949image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 87288
14.5%
5 84861
14.1%
6 70331
11.7%
7 57031
9.5%
3 50085
8.3%
8 44035
7.3%
9 34534
 
5.7%
10 27095
 
4.5%
11 21595
 
3.6%
12 17110
 
2.8%
Other values (157) 99750
16.5%
ValueCountFrequency (%)
1 748
 
0.1%
2 11947
 
2.0%
3 50085
8.3%
4 87288
14.5%
5 84861
14.1%
6 70331
11.7%
7 57031
9.5%
8 44035
7.3%
9 34534
 
5.7%
10 27095
 
4.5%
ValueCountFrequency (%)
223 1
< 0.1%
189 1
< 0.1%
188 2
< 0.1%
182 2
< 0.1%
181 1
< 0.1%
179 1
< 0.1%
178 1
< 0.1%
173 2
< 0.1%
172 1
< 0.1%
171 2
< 0.1%

CRS_ARR_TIME
Real number (ℝ)

Distinct1312
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1479.811
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T02:45:31.700386image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile702
Q11050
median1511
Q31927
95-th percentile2300
Maximum2359
Range2358
Interquartile range (IQR)877

Descriptive statistics

Standard deviation535.16184
Coefficient of variation (CV)0.36164201
Kurtosis-0.48340853
Mean1479.811
Median Absolute Deviation (MAD)424
Skewness-0.31014863
Sum8.9230682 × 108
Variance286398.2
MonotonicityNot monotonic
2024-03-30T02:45:32.047984image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2359 3603
 
0.6%
1810 2162
 
0.4%
2000 1872
 
0.3%
2100 1825
 
0.3%
1940 1746
 
0.3%
1655 1737
 
0.3%
1900 1732
 
0.3%
1400 1722
 
0.3%
1030 1655
 
0.3%
2200 1654
 
0.3%
Other values (1302) 583279
96.7%
ValueCountFrequency (%)
1 70
 
< 0.1%
2 84
 
< 0.1%
3 201
 
< 0.1%
4 162
 
< 0.1%
5 571
0.1%
6 109
 
< 0.1%
7 151
 
< 0.1%
8 104
 
< 0.1%
9 134
 
< 0.1%
10 578
0.1%
ValueCountFrequency (%)
2359 3603
0.6%
2358 753
 
0.1%
2357 753
 
0.1%
2356 523
 
0.1%
2355 1203
 
0.2%
2354 384
 
0.1%
2353 563
 
0.1%
2352 496
 
0.1%
2351 492
 
0.1%
2350 840
 
0.1%

ARR_TIME
Real number (ℝ)

MISSING 

Distinct1440
Distinct (%)0.2%
Missing9272
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean1446.2754
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T02:45:32.358748image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile559
Q11030
median1453
Q31919
95-th percentile2256
Maximum2400
Range2399
Interquartile range (IQR)889

Descriptive statistics

Standard deviation562.05235
Coefficient of variation (CV)0.38862056
Kurtosis-0.41419961
Mean1446.2754
Median Absolute Deviation (MAD)443
Skewness-0.38008702
Sum8.586754 × 108
Variance315902.85
MonotonicityNot monotonic
2024-03-30T02:45:32.714641image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1637 690
 
0.1%
1147 668
 
0.1%
1139 658
 
0.1%
2130 655
 
0.1%
1141 651
 
0.1%
1717 649
 
0.1%
1647 648
 
0.1%
1629 647
 
0.1%
1143 647
 
0.1%
2132 647
 
0.1%
Other values (1430) 587155
97.4%
(Missing) 9272
 
1.5%
ValueCountFrequency (%)
1 431
0.1%
2 378
0.1%
3 363
0.1%
4 377
0.1%
5 362
0.1%
6 367
0.1%
7 372
0.1%
8 399
0.1%
9 338
0.1%
10 362
0.1%
ValueCountFrequency (%)
2400 358
0.1%
2359 476
0.1%
2358 439
0.1%
2357 433
0.1%
2356 419
0.1%
2355 424
0.1%
2354 435
0.1%
2353 447
0.1%
2352 497
0.1%
2351 456
0.1%

ARR_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct1263
Distinct (%)0.2%
Missing10845
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean8.440266
Minimum-84
Maximum3424
Zeros10607
Zeros (%)1.8%
Negative360676
Negative (%)59.8%
Memory size4.6 MiB
2024-03-30T02:45:33.039222image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-84
5-th percentile-27
Q1-15
median-6
Q310
95-th percentile86
Maximum3424
Range3508
Interquartile range (IQR)25

Descriptive statistics

Standard deviation62.230354
Coefficient of variation (CV)7.3730323
Kurtosis231.78412
Mean8.440266
Median Absolute Deviation (MAD)11
Skewness10.835245
Sum4997836
Variance3872.6169
MonotonicityNot monotonic
2024-03-30T02:45:33.373692image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-11 16682
 
2.8%
-10 16603
 
2.8%
-13 16574
 
2.7%
-12 16424
 
2.7%
-9 16222
 
2.7%
-8 15946
 
2.6%
-14 15917
 
2.6%
-15 15485
 
2.6%
-7 15435
 
2.6%
-16 14801
 
2.5%
Other values (1253) 432053
71.7%
ValueCountFrequency (%)
-84 1
 
< 0.1%
-83 1
 
< 0.1%
-81 1
 
< 0.1%
-80 2
< 0.1%
-79 3
< 0.1%
-78 2
< 0.1%
-77 2
< 0.1%
-76 1
 
< 0.1%
-73 4
< 0.1%
-72 1
 
< 0.1%
ValueCountFrequency (%)
3424 1
< 0.1%
2998 1
< 0.1%
2962 1
< 0.1%
2923 1
< 0.1%
2913 1
< 0.1%
2910 1
< 0.1%
2815 1
< 0.1%
2721 1
< 0.1%
2685 1
< 0.1%
2653 1
< 0.1%

CANCELLED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
0.0
593815 
1.0
 
9172

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1808961
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 593815
98.5%
1.0 9172
 
1.5%

Length

2024-03-30T02:45:33.676421image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T02:45:34.075970image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 593815
98.5%
1.0 9172
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 1196802
66.2%
. 602987
33.3%
1 9172
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1808961
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1196802
66.2%
. 602987
33.3%
1 9172
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1808961
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1196802
66.2%
. 602987
33.3%
1 9172
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1808961
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1196802
66.2%
. 602987
33.3%
1 9172
 
0.5%

CANCELLATION_CODE
Categorical

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing593815
Missing (%)98.5%
Memory size4.6 MiB
B
5865 
A
2381 
C
926 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9172
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowA

Common Values

ValueCountFrequency (%)
B 5865
 
1.0%
A 2381
 
0.4%
C 926
 
0.2%
(Missing) 593815
98.5%

Length

2024-03-30T02:45:34.323603image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T02:45:34.529969image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
b 5865
63.9%
a 2381
26.0%
c 926
 
10.1%

Most occurring characters

ValueCountFrequency (%)
B 5865
63.9%
A 2381
26.0%
C 926
 
10.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9172
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 5865
63.9%
A 2381
26.0%
C 926
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9172
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 5865
63.9%
A 2381
26.0%
C 926
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9172
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 5865
63.9%
A 2381
26.0%
C 926
 
10.1%

DIVERTED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
0.0
601314 
1.0
 
1673

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1808961
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 601314
99.7%
1.0 1673
 
0.3%

Length

2024-03-30T02:45:34.768281image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T02:45:34.999784image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 601314
99.7%
1.0 1673
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1204301
66.6%
. 602987
33.3%
1 1673
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1808961
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1204301
66.6%
. 602987
33.3%
1 1673
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1808961
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1204301
66.6%
. 602987
33.3%
1 1673
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1808961
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1204301
66.6%
. 602987
33.3%
1 1673
 
0.1%

AIR_TIME
Real number (ℝ)

MISSING 

Distinct593
Distinct (%)0.1%
Missing10845
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean114.15247
Minimum8
Maximum656
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T02:45:35.257186image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile35
Q162
median96
Q3143
95-th percentile271
Maximum656
Range648
Interquartile range (IQR)81

Descriptive statistics

Standard deviation70.410635
Coefficient of variation (CV)0.61681221
Kurtosis2.0607336
Mean114.15247
Median Absolute Deviation (MAD)38
Skewness1.3590136
Sum67594469
Variance4957.6575
MonotonicityNot monotonic
2024-03-30T02:45:35.587508image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 5085
 
0.8%
63 5024
 
0.8%
61 5016
 
0.8%
64 4999
 
0.8%
60 4913
 
0.8%
65 4838
 
0.8%
59 4830
 
0.8%
54 4805
 
0.8%
66 4771
 
0.8%
58 4764
 
0.8%
Other values (583) 543097
90.1%
(Missing) 10845
 
1.8%
ValueCountFrequency (%)
8 9
 
< 0.1%
9 17
 
< 0.1%
10 19
 
< 0.1%
11 10
 
< 0.1%
12 3
 
< 0.1%
13 7
 
< 0.1%
14 16
 
< 0.1%
15 24
 
< 0.1%
16 93
< 0.1%
17 210
< 0.1%
ValueCountFrequency (%)
656 1
 
< 0.1%
644 2
< 0.1%
642 2
< 0.1%
641 3
< 0.1%
640 1
 
< 0.1%
639 1
 
< 0.1%
637 2
< 0.1%
635 1
 
< 0.1%
633 2
< 0.1%
631 2
< 0.1%

CARRIER_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct955
Distinct (%)0.7%
Missing474548
Missing (%)78.7%
Infinite0
Infinite (%)0.0%
Mean26.88382
Minimum0
Maximum3424
Zeros55325
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T02:45:36.312281image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q323
95-th percentile108.1
Maximum3424
Range3424
Interquartile range (IQR)23

Descriptive statistics

Standard deviation84.94507
Coefficient of variation (CV)3.1597098
Kurtosis214.81089
Mean26.88382
Median Absolute Deviation (MAD)4
Skewness11.258762
Sum3452931
Variance7215.6649
MonotonicityNot monotonic
2024-03-30T02:45:36.633017image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 55325
 
9.2%
1 2348
 
0.4%
2 2338
 
0.4%
3 2286
 
0.4%
6 2265
 
0.4%
15 2207
 
0.4%
4 2129
 
0.4%
7 2122
 
0.4%
5 2117
 
0.4%
8 1948
 
0.3%
Other values (945) 53354
 
8.8%
(Missing) 474548
78.7%
ValueCountFrequency (%)
0 55325
9.2%
1 2348
 
0.4%
2 2338
 
0.4%
3 2286
 
0.4%
4 2129
 
0.4%
5 2117
 
0.4%
6 2265
 
0.4%
7 2122
 
0.4%
8 1948
 
0.3%
9 1810
 
0.3%
ValueCountFrequency (%)
3424 1
< 0.1%
2998 1
< 0.1%
2962 1
< 0.1%
2923 1
< 0.1%
2913 1
< 0.1%
2894 1
< 0.1%
2815 1
< 0.1%
2721 1
< 0.1%
2685 1
< 0.1%
2653 1
< 0.1%

WEATHER_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct429
Distinct (%)0.3%
Missing474548
Missing (%)78.7%
Infinite0
Infinite (%)0.0%
Mean3.7365286
Minimum0
Maximum1561
Zeros120952
Zeros (%)20.1%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T02:45:36.938014image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10
Maximum1561
Range1561
Interquartile range (IQR)0

Descriptive statistics

Standard deviation28.404783
Coefficient of variation (CV)7.6019176
Kurtosis561.3963
Mean3.7365286
Median Absolute Deviation (MAD)0
Skewness19.219888
Sum479916
Variance806.83169
MonotonicityNot monotonic
2024-03-30T02:45:37.248033image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 120952
 
20.1%
15 170
 
< 0.1%
19 146
 
< 0.1%
16 144
 
< 0.1%
17 138
 
< 0.1%
18 135
 
< 0.1%
9 133
 
< 0.1%
20 133
 
< 0.1%
23 131
 
< 0.1%
22 126
 
< 0.1%
Other values (419) 6231
 
1.0%
(Missing) 474548
78.7%
ValueCountFrequency (%)
0 120952
20.1%
1 91
 
< 0.1%
2 97
 
< 0.1%
3 97
 
< 0.1%
4 99
 
< 0.1%
5 102
 
< 0.1%
6 117
 
< 0.1%
7 124
 
< 0.1%
8 121
 
< 0.1%
9 133
 
< 0.1%
ValueCountFrequency (%)
1561 1
< 0.1%
1251 1
< 0.1%
1226 1
< 0.1%
1190 1
< 0.1%
1166 1
< 0.1%
1103 2
< 0.1%
1056 2
< 0.1%
1038 1
< 0.1%
1035 1
< 0.1%
990 1
< 0.1%

NAS_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct419
Distinct (%)0.3%
Missing474548
Missing (%)78.7%
Infinite0
Infinite (%)0.0%
Mean11.504185
Minimum0
Maximum1316
Zeros72188
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T02:45:37.547563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q314
95-th percentile52
Maximum1316
Range1316
Interquartile range (IQR)14

Descriptive statistics

Standard deviation29.91226
Coefficient of variation (CV)2.6001199
Kurtosis225.86876
Mean11.504185
Median Absolute Deviation (MAD)0
Skewness10.223394
Sum1477586
Variance894.74331
MonotonicityNot monotonic
2024-03-30T02:45:37.838374image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 72188
 
12.0%
1 3422
 
0.6%
2 2405
 
0.4%
3 2193
 
0.4%
4 2045
 
0.3%
15 2028
 
0.3%
5 1979
 
0.3%
16 1825
 
0.3%
6 1785
 
0.3%
17 1669
 
0.3%
Other values (409) 36900
 
6.1%
(Missing) 474548
78.7%
ValueCountFrequency (%)
0 72188
12.0%
1 3422
 
0.6%
2 2405
 
0.4%
3 2193
 
0.4%
4 2045
 
0.3%
5 1979
 
0.3%
6 1785
 
0.3%
7 1652
 
0.3%
8 1669
 
0.3%
9 1474
 
0.2%
ValueCountFrequency (%)
1316 1
< 0.1%
1227 1
< 0.1%
1108 1
< 0.1%
1084 1
< 0.1%
1041 1
< 0.1%
1027 1
< 0.1%
1020 1
< 0.1%
981 1
< 0.1%
962 1
< 0.1%
937 1
< 0.1%

SECURITY_DELAY
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct103
Distinct (%)0.1%
Missing474548
Missing (%)78.7%
Infinite0
Infinite (%)0.0%
Mean0.16595427
Minimum0
Maximum805
Zeros127603
Zeros (%)21.2%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T02:45:38.245422image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum805
Range805
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.6060325
Coefficient of variation (CV)21.729074
Kurtosis19792.056
Mean0.16595427
Median Absolute Deviation (MAD)0
Skewness101.13223
Sum21315
Variance13.00347
MonotonicityNot monotonic
2024-03-30T02:45:38.556383image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 127603
 
21.2%
15 40
 
< 0.1%
17 35
 
< 0.1%
16 34
 
< 0.1%
14 30
 
< 0.1%
20 28
 
< 0.1%
5 27
 
< 0.1%
10 27
 
< 0.1%
7 27
 
< 0.1%
3 26
 
< 0.1%
Other values (93) 562
 
0.1%
(Missing) 474548
78.7%
ValueCountFrequency (%)
0 127603
21.2%
1 18
 
< 0.1%
2 16
 
< 0.1%
3 26
 
< 0.1%
4 15
 
< 0.1%
5 27
 
< 0.1%
6 18
 
< 0.1%
7 27
 
< 0.1%
8 19
 
< 0.1%
9 25
 
< 0.1%
ValueCountFrequency (%)
805 1
< 0.1%
187 1
< 0.1%
178 1
< 0.1%
173 1
< 0.1%
171 1
< 0.1%
170 1
< 0.1%
138 1
< 0.1%
136 1
< 0.1%
135 1
< 0.1%
126 1
< 0.1%

LATE_AIRCRAFT_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct792
Distinct (%)0.6%
Missing474548
Missing (%)78.7%
Infinite0
Infinite (%)0.0%
Mean30.897593
Minimum0
Maximum1865
Zeros59380
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T02:45:38.959323image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q335
95-th percentile132
Maximum1865
Range1865
Interquartile range (IQR)35

Descriptive statistics

Standard deviation68.645197
Coefficient of variation (CV)2.2217005
Kurtosis92.569107
Mean30.897593
Median Absolute Deviation (MAD)6
Skewness7.3588678
Sum3968456
Variance4712.1631
MonotonicityNot monotonic
2024-03-30T02:45:39.242183image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 59380
 
9.8%
15 1726
 
0.3%
16 1542
 
0.3%
17 1496
 
0.2%
18 1461
 
0.2%
20 1312
 
0.2%
19 1296
 
0.2%
21 1252
 
0.2%
22 1231
 
0.2%
14 1184
 
0.2%
Other values (782) 56559
 
9.4%
(Missing) 474548
78.7%
ValueCountFrequency (%)
0 59380
9.8%
1 844
 
0.1%
2 847
 
0.1%
3 838
 
0.1%
4 861
 
0.1%
5 884
 
0.1%
6 951
 
0.2%
7 925
 
0.2%
8 962
 
0.2%
9 1003
 
0.2%
ValueCountFrequency (%)
1865 1
< 0.1%
1790 1
< 0.1%
1722 1
< 0.1%
1703 1
< 0.1%
1565 1
< 0.1%
1521 1
< 0.1%
1387 1
< 0.1%
1382 1
< 0.1%
1371 1
< 0.1%
1360 1
< 0.1%

Interactions

2024-03-30T02:44:39.480038image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:18.285501image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:25.711938image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:33.014499image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:40.113915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:46.414331image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:53.443860image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:00.228446image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:08.027221image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:15.057566image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:22.418175image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:29.584681image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:36.756541image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:43.348192image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:50.489288image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:57.398740image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:04.150277image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:10.276264image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:15.901281image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:30.837160image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:39.777735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:18.800473image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:26.090204image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:33.352342image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:40.465855image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:46.890891image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:53.772261image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:00.624930image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:08.410665image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:15.438553image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:22.761988image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:29.918088image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:37.105568image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:43.691097image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:50.823690image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:57.730718image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:04.543922image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:10.544889image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:16.217715image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:31.370922image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:40.105669image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:19.135799image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:26.419642image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:33.688405image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:40.773544image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:47.207907image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:54.131298image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:01.018842image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:08.748021image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:15.822740image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:23.112041image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:30.297029image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:37.446538image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:43.977046image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:51.176403image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:58.074322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:04.973879image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:10.815202image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:16.474974image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:32.108609image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:40.670669image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:19.494218image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:26.770415image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:34.054815image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:41.097994image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:47.555337image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:54.480379image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:01.421436image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:09.133433image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:16.260584image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:23.491278image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:30.635214image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:37.806159image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:44.387481image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:51.549629image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:58.442255image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:05.285584image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:11.109764image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:16.765451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:32.475528image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:41.145093image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:19.820155image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:27.102491image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:34.408764image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:41.398286image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:47.889444image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:54.813700image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:01.814250image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:09.635727image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:16.633948image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:23.836143image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:30.977269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:38.164743image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:44.698323image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:51.912019image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:58.756405image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:05.594797image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:11.359676image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:17.045417image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:32.938145image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:41.607279image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:20.212068image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:27.598478image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:34.770291image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:41.730832image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:48.301454image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:55.165990image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:02.263660image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:10.017734image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:17.266727image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:24.261759image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:31.311755image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:38.516957image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:45.029953image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:52.321831image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:59.107895image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:05.944957image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:11.659224image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:17.386567image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:33.317396image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:42.023457image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:20.591778image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:27.974017image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:35.110617image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:42.077242image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:48.623349image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:55.484320image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:02.624797image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:10.387435image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:17.731524image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:24.614369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:31.639852image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:38.854020image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:45.346709image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T02:43:56.239694image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:02.840969image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:09.112890image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:14.767762image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:23.699243image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:37.661908image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:50.599671image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:24.719965image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:32.110426image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:39.098014image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:45.422466image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:52.433513image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:59.229163image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:07.062062image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:14.065346image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:21.376424image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:28.550257image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:35.649216image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:42.450402image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:49.493356image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:56.505221image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:03.153234image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:09.407943image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:15.078873image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:25.592022image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:38.091578image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:51.041144image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:25.024991image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:32.361224image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:39.389061image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:45.691695image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:52.718242image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:59.504070image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:07.337868image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:14.327647image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:21.674491image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:28.803258image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:35.946455image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:42.695125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:49.757794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:56.753793image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:03.457312image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:09.660241image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:15.343437image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:27.692903image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:38.472968image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:51.509582image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:25.325033image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:32.658438image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:39.677385image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:45.968504image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:53.031184image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:42:59.748353image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:07.621642image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:14.627469image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:21.973788image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:29.103628image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:36.383631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:42.967792image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:50.095184image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:43:57.027445image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:03.786954image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:09.968772image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:15.632653image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:29.388710image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T02:44:39.149228image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-30T02:44:53.773203image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T02:45:00.062129image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DAY_OF_WEEKFL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_NMORIGIN_WACDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_NMDEST_WACCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDAIR_TIMECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAY
018/7/2023 12:00:00 AM9E490014524RICRichmond, VAVirginia3813487MSPMinneapolis, MNMinnesota63619614.0-5.016.05.0819810.0-9.00.0NaN0.0155.0NaNNaNNaNNaNNaN
118/7/2023 12:00:00 AM9E490111057CLTCharlotte, NCNorth Carolina3612478JFKNew York, NYNew York221955NaNNaNNaNNaN2210NaNNaN1.0B0.0NaNNaNNaNNaNNaNNaN
218/7/2023 12:00:00 AM9E490112478JFKNew York, NYNew York2211057CLTCharlotte, NCNorth Carolina361629NaNNaNNaNNaN1907NaNNaN1.0B0.0NaNNaNNaNNaNNaNNaN
318/7/2023 12:00:00 AM9E490215096SYRSyracuse, NYNew York2211433DTWDetroit, MIMichigan43615615.00.011.07.0746735.0-11.00.0NaN0.062.0NaNNaNNaNNaNNaN
418/7/2023 12:00:00 AM9E490310599BHMBirmingham, ALAlabama5112953LGANew York, NYNew York2211091105.0-4.022.013.014411435.0-6.00.0NaN0.0115.0NaNNaNNaNNaNNaN
518/7/2023 12:00:00 AM9E490312953LGANew York, NYNew York2210599BHMBirmingham, ALAlabama51841832.0-9.022.06.010221005.0-17.00.0NaN0.0125.0NaNNaNNaNNaNNaN
618/7/2023 12:00:00 AM9E490410581BGRBangor, MEMaine1212953LGANew York, NYNew York2212211215.0-6.09.06.014041336.0-28.00.0NaN0.066.0NaNNaNNaNNaNNaN
718/7/2023 12:00:00 AM9E490412953LGANew York, NYNew York2210581BGRBangor, MEMaine12945935.0-10.014.018.011341100.0-34.00.0NaN0.053.0NaNNaNNaNNaNNaN
818/7/2023 12:00:00 AM9E490512953LGANew York, NYNew York2211267DAYDayton, OHOhio4420002033.033.045.06.022072305.058.00.0NaN0.0101.00.00.058.00.00.0
918/7/2023 12:00:00 AM9E490610397ATLAtlanta, GAGeorgia3413377MLUMonroe, LALouisiana7210051000.0-5.015.02.010431025.0-18.00.0NaN0.068.0NaNNaNNaNNaNNaN
DAY_OF_WEEKFL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_NMORIGIN_WACDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_NMDEST_WACCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDAIR_TIMECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAY
60297778/27/2023 12:00:00 AMYX583212339INDIndianapolis, INIndiana4212953LGANew York, NYNew York2214051400.0-5.017.028.016101611.01.00.0NaN0.086.0NaNNaNNaNNaNNaN
60297878/27/2023 12:00:00 AMYX583212953LGANew York, NYNew York2212339INDIndianapolis, INIndiana4210511046.0-5.015.06.013171249.0-28.00.0NaN0.0102.0NaNNaNNaNNaNNaN
60297978/27/2023 12:00:00 AMYX583312478JFKNew York, NYNew York2211066CMHColumbus, OHOhio4421302122.0-8.048.04.023352332.0-3.00.0NaN0.078.0NaNNaNNaNNaNNaN
60298078/27/2023 12:00:00 AMYX583412953LGANew York, NYNew York2213485MSNMadison, WIWisconsin4520502050.00.024.05.022242220.0-4.00.0NaN0.0121.0NaNNaNNaNNaNNaN
60298178/27/2023 12:00:00 AMYX583711057CLTCharlotte, NCNorth Carolina3612478JFKNew York, NYNew York22635629.0-6.021.09.0849817.0-32.00.0NaN0.078.0NaNNaNNaNNaNNaN
60298278/27/2023 12:00:00 AMYX583812339INDIndianapolis, INIndiana4210721BOSBoston, MAMassachusetts1317001654.0-6.017.019.019351921.0-14.00.0NaN0.0111.0NaNNaNNaNNaNNaN
60298378/27/2023 12:00:00 AMYX583910721BOSBoston, MAMassachusetts1311066CMHColumbus, OHOhio4414001409.09.019.07.016151619.04.00.0NaN0.0104.0NaNNaNNaNNaNNaN
60298478/27/2023 12:00:00 AMYX584412953LGANew York, NYNew York2210721BOSBoston, MAMassachusetts1311001056.0-4.024.011.012301207.0-23.00.0NaN0.036.0NaNNaNNaNNaNNaN
60298578/27/2023 12:00:00 AMYX584510154ACKNantucket, MAMassachusetts1312478JFKNew York, NYNew York2211521143.0-9.010.07.013091244.0-25.00.0NaN0.044.0NaNNaNNaNNaNNaN
60298678/27/2023 12:00:00 AMYX584611066CMHColumbus, OHOhio4412478JFKNew York, NYNew York2211451143.0-2.012.015.013591332.0-27.00.0NaN0.082.0NaNNaNNaNNaNNaN